Face Generation

Student: Angel Martinez-Tenor
Deep Learning Nanodegree Foundation - Udacity
May 13, 2017

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fe0797dc4a8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fe0796cd320>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # EXERCISE: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learing_rate = tf.placeholder(tf.float32, name='learning_rate')

    return inputs_real, inputs_z, learing_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # EXERCISE: Implement Function
    
    # Adapted from DCGAN.ipynb Discriminator. Number of Convolutional layers tested: 2-3
    
    alpha = 0.2
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x1 (MNIST) or 28x28x3 (ceb faces)
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        bn1 = tf.layers.batch_normalization(x1, training=True)
        relu1 = tf.maximum(alpha * bn1, bn1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x128  
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='valid')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
#       # 4x4x256   

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits 

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # EXERCISE: Implement Function
    
    # Adapted from DCGAN.ipynb Generator. Number of Convolutional layers tested: 2-3
    
    alpha = 0.2
    
    with tf.variable_scope('generator', reuse= not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 2*2*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 2, 2, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 2x2x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 7x7x256 now

        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
#         # 14x14x128 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        # 28x28x1 (MNIST) or 28x28x3 (ceb faces)  now
        
        out = tf.tanh(logits)
        
        return out    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # EXERCISE: Implement Function
    
    # Adapted from DCGAN.ipynb Model Loss
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    soft = 0.9 # one-sided label smoothing

    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
                                                                         labels=tf.ones_like(d_model_real)*soft))
    
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                                         labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                                    labels=tf.ones_like(d_model_fake)*soft))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # EXERCISE: Implement Function
    
    # From DCGAN.ipynb Optimizers
    
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    
    # 'with tf.control_dependencie' is required for bacth normalization
    #  https://github.com/udacity/deep-learning/blob/master/batch-norm/Batch_Normalization_Lesson.ipynb
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):  
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)


    return d_train_opt, g_train_opt  
        
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
    #example_z = np.random.normal(1, 1, size=[n_images, z_dim])


    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # EXERCISE: Build Model
    
    # Adapted from DCGAN.ipynb Building the model, GAN class

    # tf.reset_default_graph() cannot be used here as train is called within tf.Graph().as_default()
    
    #print(data_shape)
    assert ((data_image_mode == "L" and data_shape[3] == 1) or 
            (data_image_mode == "RGB" and data_shape[3] == 3)), \
            ("Image mode Inconsistency: ", data_image_mode, " mode, ", data_shape[2], " channels")
    out_channel_dim = data_shape[3]    
        
    input_real, input_z, lr_placeholder = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)        
    d_loss, g_loss = model_loss(input_real, input_z, out_channel_dim)
    d_opt, g_opt = model_opt(d_loss, g_loss, lr_placeholder, beta1)    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        samples, losses = [], []
        steps = 0
        show_every = 100
        print_every = 30
        sample_z = np.random.uniform(-1, 1, size=(50, z_dim))
        # sample_z = np.random.normal(0, 1, size=(50, z_dim))
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                
                # EXERCISE: Train Model
                
                # Adapted from DCGAN.ipynb Building the model, train function
                
                steps += 1

                batch_images *= 2  # From [-0.5,0.5] to -[1,1]
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                #batch_z = np.random.normal(0, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr_placeholder: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, lr_placeholder: learning_rate, input_real: batch_images})
                    # 2x generator optimizer as suggested in the forum
                # _ = sess.run(g_opt, feed_dict={input_z: batch_z, lr_placeholder: learning_rate}) 

                
                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images, lr_placeholder: learning_rate})
                    train_loss_g = g_loss.eval({input_z: batch_z, lr_placeholder: learning_rate})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))

                if steps % show_every == 0:
                          
                    # show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode)
                    show_generator_output(sess, 25, input_z, out_channel_dim, data_image_mode)                  

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 32        # tested from 32 to 256
z_dim = 100            # tested from 50 to 200
learning_rate = 0.0002 # tested from 0.00001 to 0.02  
beta1 = 0.1            # tested from 0.01 to 0.7

# Note: The parameters have been tuned to generate accurate handwritten digits. 
#  Lower generator losses can be achieved with worse images.   

tf.reset_default_graph()  # added

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 6.2382... Generator Loss: 0.5772
Epoch 1/2... Discriminator Loss: 3.2432... Generator Loss: 0.3491
Epoch 1/2... Discriminator Loss: 1.0796... Generator Loss: 1.3028
Epoch 1/2... Discriminator Loss: 1.7167... Generator Loss: 0.5350
Epoch 1/2... Discriminator Loss: 1.3964... Generator Loss: 0.5171
Epoch 1/2... Discriminator Loss: 1.6069... Generator Loss: 0.4630
Epoch 1/2... Discriminator Loss: 1.8403... Generator Loss: 0.3827
Epoch 1/2... Discriminator Loss: 1.9425... Generator Loss: 0.3755
Epoch 1/2... Discriminator Loss: 1.1785... Generator Loss: 0.7736
Epoch 1/2... Discriminator Loss: 1.5734... Generator Loss: 0.5015
Epoch 1/2... Discriminator Loss: 1.0204... Generator Loss: 0.7944
Epoch 1/2... Discriminator Loss: 1.5913... Generator Loss: 0.4377
Epoch 1/2... Discriminator Loss: 1.3477... Generator Loss: 0.5355
Epoch 1/2... Discriminator Loss: 0.9224... Generator Loss: 1.0986
Epoch 1/2... Discriminator Loss: 1.1255... Generator Loss: 0.7338
Epoch 1/2... Discriminator Loss: 0.8677... Generator Loss: 1.0749
Epoch 1/2... Discriminator Loss: 1.7747... Generator Loss: 0.4129
Epoch 1/2... Discriminator Loss: 1.2085... Generator Loss: 2.8302
Epoch 1/2... Discriminator Loss: 1.5915... Generator Loss: 2.6766
Epoch 1/2... Discriminator Loss: 1.7915... Generator Loss: 0.4160
Epoch 1/2... Discriminator Loss: 1.0090... Generator Loss: 1.1102
Epoch 1/2... Discriminator Loss: 1.2344... Generator Loss: 0.7159
Epoch 1/2... Discriminator Loss: 1.8502... Generator Loss: 0.3922
Epoch 1/2... Discriminator Loss: 0.7805... Generator Loss: 1.3105
Epoch 1/2... Discriminator Loss: 1.7073... Generator Loss: 0.4075
Epoch 1/2... Discriminator Loss: 1.0086... Generator Loss: 0.7591
Epoch 1/2... Discriminator Loss: 0.8872... Generator Loss: 1.5923
Epoch 1/2... Discriminator Loss: 0.7361... Generator Loss: 2.2310
Epoch 1/2... Discriminator Loss: 1.0938... Generator Loss: 1.6736
Epoch 1/2... Discriminator Loss: 0.9259... Generator Loss: 0.8809
Epoch 1/2... Discriminator Loss: 0.9165... Generator Loss: 0.8978
Epoch 1/2... Discriminator Loss: 1.0855... Generator Loss: 0.7369
Epoch 1/2... Discriminator Loss: 0.8792... Generator Loss: 1.5673
Epoch 1/2... Discriminator Loss: 0.8284... Generator Loss: 1.4743
Epoch 1/2... Discriminator Loss: 0.8835... Generator Loss: 0.9779
Epoch 1/2... Discriminator Loss: 0.9710... Generator Loss: 2.3204
Epoch 1/2... Discriminator Loss: 1.1745... Generator Loss: 0.6448
Epoch 1/2... Discriminator Loss: 1.0264... Generator Loss: 0.7602
Epoch 1/2... Discriminator Loss: 0.8529... Generator Loss: 0.9945
Epoch 1/2... Discriminator Loss: 1.3678... Generator Loss: 0.5579
Epoch 1/2... Discriminator Loss: 1.2923... Generator Loss: 0.5673
Epoch 1/2... Discriminator Loss: 0.9133... Generator Loss: 1.0415
Epoch 1/2... Discriminator Loss: 1.0758... Generator Loss: 0.7893
Epoch 1/2... Discriminator Loss: 0.7653... Generator Loss: 1.1835
Epoch 1/2... Discriminator Loss: 1.2971... Generator Loss: 0.5719
Epoch 1/2... Discriminator Loss: 1.6734... Generator Loss: 0.4487
Epoch 1/2... Discriminator Loss: 1.1112... Generator Loss: 0.6581
Epoch 1/2... Discriminator Loss: 0.6863... Generator Loss: 1.3110
Epoch 1/2... Discriminator Loss: 0.8782... Generator Loss: 1.1342
Epoch 1/2... Discriminator Loss: 0.8014... Generator Loss: 1.7392
Epoch 1/2... Discriminator Loss: 1.7600... Generator Loss: 0.4333
Epoch 1/2... Discriminator Loss: 0.9705... Generator Loss: 0.7958
Epoch 1/2... Discriminator Loss: 1.1461... Generator Loss: 0.6367
Epoch 1/2... Discriminator Loss: 1.1846... Generator Loss: 3.0719
Epoch 1/2... Discriminator Loss: 1.2595... Generator Loss: 0.5878
Epoch 1/2... Discriminator Loss: 1.8534... Generator Loss: 2.7137
Epoch 1/2... Discriminator Loss: 0.7242... Generator Loss: 1.1124
Epoch 1/2... Discriminator Loss: 0.7525... Generator Loss: 1.1620
Epoch 1/2... Discriminator Loss: 1.1030... Generator Loss: 0.7421
Epoch 1/2... Discriminator Loss: 0.9409... Generator Loss: 0.8519
Epoch 1/2... Discriminator Loss: 1.0213... Generator Loss: 0.7918
Epoch 1/2... Discriminator Loss: 1.5960... Generator Loss: 0.4870
Epoch 2/2... Discriminator Loss: 0.7375... Generator Loss: 1.2250
Epoch 2/2... Discriminator Loss: 1.1272... Generator Loss: 0.6954
Epoch 2/2... Discriminator Loss: 1.3345... Generator Loss: 0.5605
Epoch 2/2... Discriminator Loss: 0.7241... Generator Loss: 1.2054
Epoch 2/2... Discriminator Loss: 1.6821... Generator Loss: 0.5033
Epoch 2/2... Discriminator Loss: 0.7544... Generator Loss: 1.9968
Epoch 2/2... Discriminator Loss: 0.7495... Generator Loss: 1.0636
Epoch 2/2... Discriminator Loss: 1.1773... Generator Loss: 0.7863
Epoch 2/2... Discriminator Loss: 0.7680... Generator Loss: 2.9383
Epoch 2/2... Discriminator Loss: 0.9712... Generator Loss: 0.7921
Epoch 2/2... Discriminator Loss: 1.3022... Generator Loss: 0.6278
Epoch 2/2... Discriminator Loss: 0.6811... Generator Loss: 1.2834
Epoch 2/2... Discriminator Loss: 0.5634... Generator Loss: 1.5780
Epoch 2/2... Discriminator Loss: 0.6445... Generator Loss: 1.5825
Epoch 2/2... Discriminator Loss: 0.6766... Generator Loss: 1.2712
Epoch 2/2... Discriminator Loss: 0.7447... Generator Loss: 1.0955
Epoch 2/2... Discriminator Loss: 0.8050... Generator Loss: 0.9803
Epoch 2/2... Discriminator Loss: 1.2186... Generator Loss: 0.5769
Epoch 2/2... Discriminator Loss: 0.5533... Generator Loss: 2.1291
Epoch 2/2... Discriminator Loss: 0.6016... Generator Loss: 1.7194
Epoch 2/2... Discriminator Loss: 1.1496... Generator Loss: 0.6935
Epoch 2/2... Discriminator Loss: 0.5444... Generator Loss: 2.3032
Epoch 2/2... Discriminator Loss: 0.5452... Generator Loss: 1.6824
Epoch 2/2... Discriminator Loss: 0.8443... Generator Loss: 0.9736
Epoch 2/2... Discriminator Loss: 0.5785... Generator Loss: 1.6781
Epoch 2/2... Discriminator Loss: 0.6959... Generator Loss: 1.2318
Epoch 2/2... Discriminator Loss: 0.7822... Generator Loss: 2.0790
Epoch 2/2... Discriminator Loss: 1.7830... Generator Loss: 0.4485
Epoch 2/2... Discriminator Loss: 0.6583... Generator Loss: 1.7407
Epoch 2/2... Discriminator Loss: 0.9256... Generator Loss: 1.0115
Epoch 2/2... Discriminator Loss: 0.6298... Generator Loss: 1.4252
Epoch 2/2... Discriminator Loss: 0.5313... Generator Loss: 1.6201
Epoch 2/2... Discriminator Loss: 0.7735... Generator Loss: 1.1969
Epoch 2/2... Discriminator Loss: 0.5563... Generator Loss: 1.9172
Epoch 2/2... Discriminator Loss: 0.9142... Generator Loss: 0.8576
Epoch 2/2... Discriminator Loss: 0.9217... Generator Loss: 1.0342
Epoch 2/2... Discriminator Loss: 0.6825... Generator Loss: 1.3179
Epoch 2/2... Discriminator Loss: 1.1789... Generator Loss: 0.6855
Epoch 2/2... Discriminator Loss: 1.0109... Generator Loss: 0.8367
Epoch 2/2... Discriminator Loss: 0.9778... Generator Loss: 0.8360
Epoch 2/2... Discriminator Loss: 1.0989... Generator Loss: 0.7781
Epoch 2/2... Discriminator Loss: 0.7383... Generator Loss: 1.2065
Epoch 2/2... Discriminator Loss: 1.0549... Generator Loss: 0.7275
Epoch 2/2... Discriminator Loss: 1.4148... Generator Loss: 0.5416
Epoch 2/2... Discriminator Loss: 0.8967... Generator Loss: 1.0830
Epoch 2/2... Discriminator Loss: 0.9932... Generator Loss: 0.8399
Epoch 2/2... Discriminator Loss: 1.7005... Generator Loss: 0.4647
Epoch 2/2... Discriminator Loss: 1.0043... Generator Loss: 0.8763
Epoch 2/2... Discriminator Loss: 0.5147... Generator Loss: 1.9104
Epoch 2/2... Discriminator Loss: 0.7885... Generator Loss: 1.0188
Epoch 2/2... Discriminator Loss: 0.6302... Generator Loss: 2.5859
Epoch 2/2... Discriminator Loss: 0.6951... Generator Loss: 1.4153
Epoch 2/2... Discriminator Loss: 0.7869... Generator Loss: 1.1046
Epoch 2/2... Discriminator Loss: 0.6306... Generator Loss: 2.2901
Epoch 2/2... Discriminator Loss: 1.1523... Generator Loss: 0.9150
Epoch 2/2... Discriminator Loss: 0.6903... Generator Loss: 1.2975
Epoch 2/2... Discriminator Loss: 0.6720... Generator Loss: 2.0891
Epoch 2/2... Discriminator Loss: 0.6406... Generator Loss: 1.3543
Epoch 2/2... Discriminator Loss: 0.5590... Generator Loss: 1.6346
Epoch 2/2... Discriminator Loss: 0.6114... Generator Loss: 1.5344
Epoch 2/2... Discriminator Loss: 0.5104... Generator Loss: 2.3592
Epoch 2/2... Discriminator Loss: 0.6276... Generator Loss: 1.4244
Epoch 2/2... Discriminator Loss: 0.6676... Generator Loss: 1.3481

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 32        # tested from 32 to 256         
z_dim = 100            # tested from 50 to 200
learning_rate = 0.0002 #tested from 0.00001 to 0.02
beta1 = 0.1            # tested from 0.01 to 0.7

tf.reset_default_graph() # added

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.8329... Generator Loss: 0.4063
Epoch 1/1... Discriminator Loss: 1.9068... Generator Loss: 0.3988
Epoch 1/1... Discriminator Loss: 2.5310... Generator Loss: 8.4453
Epoch 1/1... Discriminator Loss: 0.3829... Generator Loss: 3.1674
Epoch 1/1... Discriminator Loss: 1.5012... Generator Loss: 0.5170
Epoch 1/1... Discriminator Loss: 1.6615... Generator Loss: 0.4417
Epoch 1/1... Discriminator Loss: 1.3981... Generator Loss: 0.6125
Epoch 1/1... Discriminator Loss: 0.8659... Generator Loss: 1.3271
Epoch 1/1... Discriminator Loss: 1.4617... Generator Loss: 0.4861
Epoch 1/1... Discriminator Loss: 2.0338... Generator Loss: 0.3646
Epoch 1/1... Discriminator Loss: 1.0053... Generator Loss: 0.8249
Epoch 1/1... Discriminator Loss: 1.1522... Generator Loss: 0.8485
Epoch 1/1... Discriminator Loss: 1.0715... Generator Loss: 0.7867
Epoch 1/1... Discriminator Loss: 1.4246... Generator Loss: 0.5046
Epoch 1/1... Discriminator Loss: 1.0660... Generator Loss: 1.3151
Epoch 1/1... Discriminator Loss: 0.8701... Generator Loss: 1.0445
Epoch 1/1... Discriminator Loss: 0.9636... Generator Loss: 1.0007
Epoch 1/1... Discriminator Loss: 1.5911... Generator Loss: 0.4404
Epoch 1/1... Discriminator Loss: 1.7740... Generator Loss: 0.4027
Epoch 1/1... Discriminator Loss: 1.3269... Generator Loss: 1.3496
Epoch 1/1... Discriminator Loss: 1.1644... Generator Loss: 1.0701
Epoch 1/1... Discriminator Loss: 1.2441... Generator Loss: 0.6268
Epoch 1/1... Discriminator Loss: 1.5555... Generator Loss: 0.4543
Epoch 1/1... Discriminator Loss: 1.1170... Generator Loss: 0.7789
Epoch 1/1... Discriminator Loss: 1.4693... Generator Loss: 0.4652
Epoch 1/1... Discriminator Loss: 1.4922... Generator Loss: 0.4968
Epoch 1/1... Discriminator Loss: 1.4157... Generator Loss: 0.5100
Epoch 1/1... Discriminator Loss: 1.0651... Generator Loss: 0.7056
Epoch 1/1... Discriminator Loss: 1.6074... Generator Loss: 0.4463
Epoch 1/1... Discriminator Loss: 1.2296... Generator Loss: 0.6695
Epoch 1/1... Discriminator Loss: 1.2201... Generator Loss: 1.1364
Epoch 1/1... Discriminator Loss: 1.1574... Generator Loss: 0.7472
Epoch 1/1... Discriminator Loss: 0.9436... Generator Loss: 1.1204
Epoch 1/1... Discriminator Loss: 1.3798... Generator Loss: 0.5228
Epoch 1/1... Discriminator Loss: 1.0573... Generator Loss: 0.7804
Epoch 1/1... Discriminator Loss: 1.2616... Generator Loss: 0.6308
Epoch 1/1... Discriminator Loss: 1.1436... Generator Loss: 0.7127
Epoch 1/1... Discriminator Loss: 1.4466... Generator Loss: 0.5236
Epoch 1/1... Discriminator Loss: 1.8038... Generator Loss: 0.3882
Epoch 1/1... Discriminator Loss: 1.4115... Generator Loss: 0.5545
Epoch 1/1... Discriminator Loss: 1.7026... Generator Loss: 0.4191
Epoch 1/1... Discriminator Loss: 1.2487... Generator Loss: 0.5773
Epoch 1/1... Discriminator Loss: 1.0580... Generator Loss: 0.8359
Epoch 1/1... Discriminator Loss: 1.3272... Generator Loss: 0.5797
Epoch 1/1... Discriminator Loss: 1.2639... Generator Loss: 0.8765
Epoch 1/1... Discriminator Loss: 1.3399... Generator Loss: 1.1567
Epoch 1/1... Discriminator Loss: 1.1316... Generator Loss: 1.2236
Epoch 1/1... Discriminator Loss: 1.0932... Generator Loss: 1.0746
Epoch 1/1... Discriminator Loss: 2.1507... Generator Loss: 0.3522
Epoch 1/1... Discriminator Loss: 0.9921... Generator Loss: 1.0862
Epoch 1/1... Discriminator Loss: 1.2424... Generator Loss: 1.0406
Epoch 1/1... Discriminator Loss: 1.9885... Generator Loss: 0.3780
Epoch 1/1... Discriminator Loss: 1.4029... Generator Loss: 0.8600
Epoch 1/1... Discriminator Loss: 1.2672... Generator Loss: 2.2557
Epoch 1/1... Discriminator Loss: 1.8464... Generator Loss: 1.7752
Epoch 1/1... Discriminator Loss: 1.9213... Generator Loss: 2.0753
Epoch 1/1... Discriminator Loss: 0.9439... Generator Loss: 0.8916
Epoch 1/1... Discriminator Loss: 0.9000... Generator Loss: 1.2366
Epoch 1/1... Discriminator Loss: 0.9041... Generator Loss: 1.8141
Epoch 1/1... Discriminator Loss: 1.7372... Generator Loss: 0.4165
Epoch 1/1... Discriminator Loss: 1.0164... Generator Loss: 1.2956
Epoch 1/1... Discriminator Loss: 1.3956... Generator Loss: 0.9228
Epoch 1/1... Discriminator Loss: 1.2710... Generator Loss: 1.1254
Epoch 1/1... Discriminator Loss: 0.7936... Generator Loss: 1.6974
Epoch 1/1... Discriminator Loss: 1.2485... Generator Loss: 0.5639
Epoch 1/1... Discriminator Loss: 1.6702... Generator Loss: 0.4252
Epoch 1/1... Discriminator Loss: 1.4296... Generator Loss: 0.5007
Epoch 1/1... Discriminator Loss: 0.9180... Generator Loss: 1.1168
Epoch 1/1... Discriminator Loss: 1.5405... Generator Loss: 0.4991
Epoch 1/1... Discriminator Loss: 0.7805... Generator Loss: 1.5543
Epoch 1/1... Discriminator Loss: 1.6446... Generator Loss: 0.4310
Epoch 1/1... Discriminator Loss: 0.8409... Generator Loss: 1.2830
Epoch 1/1... Discriminator Loss: 1.4955... Generator Loss: 0.4687
Epoch 1/1... Discriminator Loss: 1.6575... Generator Loss: 0.4312
Epoch 1/1... Discriminator Loss: 1.6532... Generator Loss: 0.4318
Epoch 1/1... Discriminator Loss: 1.2472... Generator Loss: 1.2100
Epoch 1/1... Discriminator Loss: 1.5249... Generator Loss: 0.8641
Epoch 1/1... Discriminator Loss: 1.6628... Generator Loss: 0.4250
Epoch 1/1... Discriminator Loss: 1.4065... Generator Loss: 0.5288
Epoch 1/1... Discriminator Loss: 0.6867... Generator Loss: 1.5165
Epoch 1/1... Discriminator Loss: 1.2054... Generator Loss: 0.5841
Epoch 1/1... Discriminator Loss: 1.2881... Generator Loss: 1.4614
Epoch 1/1... Discriminator Loss: 1.2619... Generator Loss: 0.5612
Epoch 1/1... Discriminator Loss: 1.5749... Generator Loss: 0.4517
Epoch 1/1... Discriminator Loss: 1.0740... Generator Loss: 0.7650
Epoch 1/1... Discriminator Loss: 1.0448... Generator Loss: 0.8461
Epoch 1/1... Discriminator Loss: 0.8641... Generator Loss: 0.9061
Epoch 1/1... Discriminator Loss: 1.3781... Generator Loss: 0.5136
Epoch 1/1... Discriminator Loss: 0.6440... Generator Loss: 1.3402
Epoch 1/1... Discriminator Loss: 1.7066... Generator Loss: 0.4127
Epoch 1/1... Discriminator Loss: 1.2201... Generator Loss: 0.7970
Epoch 1/1... Discriminator Loss: 0.9773... Generator Loss: 0.9374
Epoch 1/1... Discriminator Loss: 1.1204... Generator Loss: 0.8094
Epoch 1/1... Discriminator Loss: 1.4992... Generator Loss: 0.4999
Epoch 1/1... Discriminator Loss: 2.0054... Generator Loss: 0.3798
Epoch 1/1... Discriminator Loss: 1.2130... Generator Loss: 0.8627
Epoch 1/1... Discriminator Loss: 1.7568... Generator Loss: 0.4090
Epoch 1/1... Discriminator Loss: 1.5885... Generator Loss: 0.4425
Epoch 1/1... Discriminator Loss: 1.0845... Generator Loss: 1.7170
Epoch 1/1... Discriminator Loss: 0.6253... Generator Loss: 1.5835
Epoch 1/1... Discriminator Loss: 0.9728... Generator Loss: 0.8534
Epoch 1/1... Discriminator Loss: 1.5987... Generator Loss: 0.4531
Epoch 1/1... Discriminator Loss: 1.9200... Generator Loss: 0.3675
Epoch 1/1... Discriminator Loss: 2.3099... Generator Loss: 0.3518
Epoch 1/1... Discriminator Loss: 1.4392... Generator Loss: 0.4973
Epoch 1/1... Discriminator Loss: 1.8606... Generator Loss: 0.3791
Epoch 1/1... Discriminator Loss: 0.9453... Generator Loss: 0.8700
Epoch 1/1... Discriminator Loss: 1.5859... Generator Loss: 0.4397
Epoch 1/1... Discriminator Loss: 0.6801... Generator Loss: 1.3711
Epoch 1/1... Discriminator Loss: 1.1735... Generator Loss: 1.3004
Epoch 1/1... Discriminator Loss: 1.6548... Generator Loss: 0.4503
Epoch 1/1... Discriminator Loss: 2.6773... Generator Loss: 2.8027
Epoch 1/1... Discriminator Loss: 2.4530... Generator Loss: 0.3324
Epoch 1/1... Discriminator Loss: 0.9614... Generator Loss: 1.3002
Epoch 1/1... Discriminator Loss: 0.9012... Generator Loss: 1.3881
Epoch 1/1... Discriminator Loss: 1.3366... Generator Loss: 0.5160
Epoch 1/1... Discriminator Loss: 0.8052... Generator Loss: 1.7876
Epoch 1/1... Discriminator Loss: 1.4697... Generator Loss: 0.5023
Epoch 1/1... Discriminator Loss: 0.6745... Generator Loss: 1.6101
Epoch 1/1... Discriminator Loss: 0.8257... Generator Loss: 2.7604
Epoch 1/1... Discriminator Loss: 0.6997... Generator Loss: 1.5236
Epoch 1/1... Discriminator Loss: 1.2975... Generator Loss: 2.3383
Epoch 1/1... Discriminator Loss: 1.5992... Generator Loss: 0.4432
Epoch 1/1... Discriminator Loss: 1.3510... Generator Loss: 3.2920
Epoch 1/1... Discriminator Loss: 1.3170... Generator Loss: 0.5619
Epoch 1/1... Discriminator Loss: 1.6322... Generator Loss: 0.4226
Epoch 1/1... Discriminator Loss: 0.5315... Generator Loss: 1.7871
Epoch 1/1... Discriminator Loss: 2.3041... Generator Loss: 2.0783
Epoch 1/1... Discriminator Loss: 0.6170... Generator Loss: 1.8086
Epoch 1/1... Discriminator Loss: 1.0913... Generator Loss: 0.6889
Epoch 1/1... Discriminator Loss: 0.7315... Generator Loss: 2.5450
Epoch 1/1... Discriminator Loss: 0.5655... Generator Loss: 1.5400
Epoch 1/1... Discriminator Loss: 2.2733... Generator Loss: 0.3515
Epoch 1/1... Discriminator Loss: 2.0900... Generator Loss: 0.3527
Epoch 1/1... Discriminator Loss: 0.6099... Generator Loss: 2.6026
Epoch 1/1... Discriminator Loss: 0.8250... Generator Loss: 3.0447
Epoch 1/1... Discriminator Loss: 1.0329... Generator Loss: 0.7307
Epoch 1/1... Discriminator Loss: 2.2254... Generator Loss: 0.3527
Epoch 1/1... Discriminator Loss: 0.6297... Generator Loss: 2.2318
Epoch 1/1... Discriminator Loss: 0.7212... Generator Loss: 1.4768
Epoch 1/1... Discriminator Loss: 0.6467... Generator Loss: 1.7505
Epoch 1/1... Discriminator Loss: 1.9777... Generator Loss: 0.3781
Epoch 1/1... Discriminator Loss: 2.2188... Generator Loss: 0.3554
Epoch 1/1... Discriminator Loss: 1.3064... Generator Loss: 0.5611
Epoch 1/1... Discriminator Loss: 0.8391... Generator Loss: 2.2407
Epoch 1/1... Discriminator Loss: 0.6747... Generator Loss: 1.6280
Epoch 1/1... Discriminator Loss: 0.6882... Generator Loss: 2.8982
Epoch 1/1... Discriminator Loss: 2.6281... Generator Loss: 0.3318
Epoch 1/1... Discriminator Loss: 1.4796... Generator Loss: 0.4886
Epoch 1/1... Discriminator Loss: 0.6272... Generator Loss: 1.9039
Epoch 1/1... Discriminator Loss: 0.8717... Generator Loss: 0.9168
Epoch 1/1... Discriminator Loss: 1.0027... Generator Loss: 0.7712
Epoch 1/1... Discriminator Loss: 1.9416... Generator Loss: 0.3717
Epoch 1/1... Discriminator Loss: 1.2493... Generator Loss: 1.4077
Epoch 1/1... Discriminator Loss: 0.7002... Generator Loss: 1.1460
Epoch 1/1... Discriminator Loss: 0.9525... Generator Loss: 2.5670
Epoch 1/1... Discriminator Loss: 0.9249... Generator Loss: 2.9160
Epoch 1/1... Discriminator Loss: 1.3921... Generator Loss: 0.5279
Epoch 1/1... Discriminator Loss: 0.7184... Generator Loss: 1.3089
Epoch 1/1... Discriminator Loss: 0.7423... Generator Loss: 1.9276
Epoch 1/1... Discriminator Loss: 2.0346... Generator Loss: 0.3705
Epoch 1/1... Discriminator Loss: 0.8719... Generator Loss: 0.8879
Epoch 1/1... Discriminator Loss: 0.6871... Generator Loss: 1.2447
Epoch 1/1... Discriminator Loss: 1.1275... Generator Loss: 0.6593
Epoch 1/1... Discriminator Loss: 1.6470... Generator Loss: 0.4306
Epoch 1/1... Discriminator Loss: 0.8837... Generator Loss: 1.3489
Epoch 1/1... Discriminator Loss: 1.9548... Generator Loss: 0.3670
Epoch 1/1... Discriminator Loss: 1.6136... Generator Loss: 1.8806
Epoch 1/1... Discriminator Loss: 0.9785... Generator Loss: 1.2383
Epoch 1/1... Discriminator Loss: 1.9796... Generator Loss: 0.3835
Epoch 1/1... Discriminator Loss: 1.8094... Generator Loss: 0.4061
Epoch 1/1... Discriminator Loss: 1.1923... Generator Loss: 0.5951
Epoch 1/1... Discriminator Loss: 0.6670... Generator Loss: 1.8330
Epoch 1/1... Discriminator Loss: 1.5174... Generator Loss: 0.4858
Epoch 1/1... Discriminator Loss: 0.6084... Generator Loss: 1.5747
Epoch 1/1... Discriminator Loss: 1.2635... Generator Loss: 2.1327
Epoch 1/1... Discriminator Loss: 1.1423... Generator Loss: 0.6492
Epoch 1/1... Discriminator Loss: 1.7313... Generator Loss: 0.4101
Epoch 1/1... Discriminator Loss: 0.8759... Generator Loss: 1.5976
Epoch 1/1... Discriminator Loss: 0.6859... Generator Loss: 1.6497
Epoch 1/1... Discriminator Loss: 2.5498... Generator Loss: 0.3345
Epoch 1/1... Discriminator Loss: 0.5843... Generator Loss: 1.4062
Epoch 1/1... Discriminator Loss: 2.5667... Generator Loss: 0.3357
Epoch 1/1... Discriminator Loss: 0.8918... Generator Loss: 1.0136
Epoch 1/1... Discriminator Loss: 0.6583... Generator Loss: 1.2224
Epoch 1/1... Discriminator Loss: 1.1778... Generator Loss: 0.6201
Epoch 1/1... Discriminator Loss: 0.5868... Generator Loss: 1.8039
Epoch 1/1... Discriminator Loss: 0.7142... Generator Loss: 1.4523
Epoch 1/1... Discriminator Loss: 0.8477... Generator Loss: 2.2796
Epoch 1/1... Discriminator Loss: 0.6297... Generator Loss: 1.4182
Epoch 1/1... Discriminator Loss: 1.8904... Generator Loss: 3.3834
Epoch 1/1... Discriminator Loss: 0.7521... Generator Loss: 1.2191
Epoch 1/1... Discriminator Loss: 0.8546... Generator Loss: 1.7002
Epoch 1/1... Discriminator Loss: 1.7824... Generator Loss: 0.4263
Epoch 1/1... Discriminator Loss: 1.2778... Generator Loss: 0.5516
Epoch 1/1... Discriminator Loss: 0.7380... Generator Loss: 1.2906
Epoch 1/1... Discriminator Loss: 1.3087... Generator Loss: 0.5504
Epoch 1/1... Discriminator Loss: 0.7349... Generator Loss: 2.4783
Epoch 1/1... Discriminator Loss: 1.2374... Generator Loss: 0.5669
Epoch 1/1... Discriminator Loss: 2.0486... Generator Loss: 2.6836
Epoch 1/1... Discriminator Loss: 1.4774... Generator Loss: 0.4954
Epoch 1/1... Discriminator Loss: 0.7999... Generator Loss: 1.8302
Epoch 1/1... Discriminator Loss: 1.0456... Generator Loss: 0.7281
Epoch 1/1... Discriminator Loss: 1.0617... Generator Loss: 0.7257
Epoch 1/1... Discriminator Loss: 1.1039... Generator Loss: 0.6663
Epoch 1/1... Discriminator Loss: 2.3415... Generator Loss: 0.3421
Epoch 1/1... Discriminator Loss: 0.9464... Generator Loss: 0.7822
Epoch 1/1... Discriminator Loss: 1.7373... Generator Loss: 0.4124
Epoch 1/1... Discriminator Loss: 0.6701... Generator Loss: 1.3153
Epoch 1/1... Discriminator Loss: 0.9975... Generator Loss: 1.8948
Epoch 1/1... Discriminator Loss: 1.0806... Generator Loss: 0.7065

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.